Who doesn’t love sitting in traffic? Especially when there’s no apparent reason for it: no crashes, no tolls, no flaming mattresses. Just a sudden and infuriating slowdown of the cars ahead, causing you to slam on the brakes, spill coffee all over yourself and slow to a glacial crawl, usually when you’re already late for something important — a job interview, for instance. Pure gridlock.And then, when all hope seems lost, the congestion breaks as seemingly spontaneously as it began. And you’re on your way again … for a good 2 minutes before the whole thing repeats itself. Welcome to the world of traffic waves, a phenomenon that’s been exasperating drivers since the first cars started coming off Ford’s assembly line a century ago.

On average, Americans spend upwards of 40 hours a year stuck in traffic, according to Texas A&M’s annual mobility study. That figure rises to more than 60 hours in some of the most congested metro areas, like Los Angeles, Washington D.C. and — yup, you guessed it — San Francisco. And, contrary to popular belief, much of this congestion is not because of major impediments, but simply a result of driving habits when there are just too many cars on the road.

The simplest explanation for why traffic waves happen is that drivers have relatively slow reaction times: if the car in front of you suddenly slows down, it’ll likely take you a second or so to hit the brakes. The slower your reaction time, the harder you have to brake to compensate and keep a safe distance. The same goes for the car behind you, which has to brake even harder than you did in order to slow down faster. And so on down the road, in a domino-like effect.

To illustrate this concept, programmer Lewis Lehe, a civil engineering graduate student at UC Berkeley, created this visualization. Select a car from the bunch, click “Hit the Brakes” to slow down your highlighted car, and then wait until a traffic wave forms. The red bars represent deceleration levels (braking) and the green, acceleration (speeding up). Mouse over a car to slow everything down see its velocity and acceleration at any given point during the wave (assuming all the cars are in the same single lane).

The equation used in the car circle above is relatively complex. Known as the Intelligent Driver Model, it was first proposed in 2000 by researchers at Germany’s Dresden University of Technology. The creators made this Java applet demonstration. Formal equations to explain these traffic patterns in terms of individual behavior are called car following models. They were first developed by researchers at General Motors in the 1950s. The simplest such formula:

where a is the car’s acceleration, Δv is the difference in velocity compared with the car behind it, T is reaction time and ƛ is some constant that researchers estimate from data. The equation says, “At time t, you accelerate at a rate proportional to the difference in speed between your car and the speed of the car you’re following, but with a gap of T seconds.”

So, put really simply, if you’re going faster than the car in front of you, then you slow down. And if you’re going slower, you speed up. This equation produces the graph below. At the 10-second mark, the grey car slows down, and the cars that brake later have to slow down to lower and lower minimum speeds. Each line shows the history of the speed of a different car. Drag the slider to graphically see a traffic wave unfold. Note how the cars at the bottom of the chart get closer together with time, as speed evens out.

Over time, congestion researchers have developed more complex models of traffic behavior that include more realistic conditions or incorporate additional data collected from traffic detectors. For example, our simple equation assumes that the car in front of you will impact your behavior even if it’s a mile away. Some of the first improvements to the equation added terms for the size of that gap and the fact that cars can slow down much faster than they can speed up. Read more about the history of car-following models here.

Lewis Lehe is a PhD student in Civil Engineering at the UC Berkeley, where he researches electronic road tolling and runs the Visualizing Urban Data idealab.

Since I don’t follow the math: It it akin to PIO? Sounds like it since it’s a feedback loop issue, having to do with the delayed reaction time and delayed system response times.

QueensWatcher

Fun to see the math confirming what I’ve been observing for decades. The solution is to allow more space between you and the car in front of you when in slow traffic and void the temptation to quickly speed up when traffic seems to start moving again. The added distance grants you more reaction time and you do not need to hit the brakes so hard; you can interrupt the wave.

MB

I always try to do that and, inevitably, someone cuts right in front of me because my cushion to not have to brake so much JUST fits their car. Frustrating. I still leave space…but it’s a case of more people needing to get on board. It’s just not human nature seemingly.

So you establish a personal wave in which you move further and further back in line, and get slower and slower until you stop? 😉

PrettyPenny

Precisely! This, combined with people trying to avoid the inevitable slowdown by switching lanes (causing even more braking) makes traffic worse for everyone. I truly believe if everyone stayed in their lane and gave ample room between cars during peak traffic hours we would see a reduction in the overall traffic time.

Calmeilles

I like to leave plenty of space between myself and the vehicle in front but all too often some other driver sees it as a gap that needs filling by changing lanes.

wbeaty

If yours is the only gap in a close-packed lane of tailgaters then of course any desperate driver is finally going arrive at your gap and make their needed lane change. But if your true goal is to block all merging, then you need to tailgate aggressively.

Be like a pro trucker. Open up lots of space and encourage merging.

rex

so basically the wingnut in front of you texting is cause all the traffic

Andy Burlingame

There’s no loss term, here. The wave keeps propagating indefinitely. Really great work, and I don’t mean to be a downer, but we’re missing some interesting dynamics.

Andy Burlingame

The lossless sim is better than the overly-lossy sim, actually.

Steven

Great Job. I would love to see this with mobile texting factored into the equation.

Mark

It already is. See lambda or T.

Val

What happens when we add Google cars? I figure a driverless car equipped with a million sensors (and maybe even communicating with other driverless cars up ahead in traffic) could react to changes in speed much sooner and smoother than humans do. My guess is that traffic at current volumes would be more efficient with driverless vehicles. I just wonder how driverless cars would deal with ice. I have driven at temperatures below freezing on dry roads and ice glazed roads. Can a driverless car anticipate reduced traction better than a human?

Lewis Lehe

Actually, you don’t need completely driverless cars for that. As adaptive cruise control becomes smarter, and the share of vehicles with adaptive cruise control rises, we should see higher traffic flows. The current rule of thumb is about 1800-2000 vehicles per lane-miles per hour on a US freeway, but some people project this could go much higher. Here is Honda’s analysis: http://world.honda.com/news/2012/4120426Traffic-Congestion-Prevent/index.html

I am not sure about ice, but that is a very important concern.

phoneranger

Does anyone have congestion-based speed limits? Seems to me if everyone on the 401 during rush hour was limited to max 35mph they’d all get where they’re going faster.

Calmeilles

It’s used on parts of London’s orbital motorway, the M25. But it does seem to be reactive rather than predictive.

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Ben Witz

Simple solution… we have an overpopulation in this world and most of the ppl that make up this overpopulation are stupid, believably asinine idiots that should never have a driver’s license to begin with. Stop that.

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For educators and the generally curious, the Lowdown is a multimedia guide to understanding big news from California and beyond. It's part of KQED's News Education project. Matthew Green is editor and project manager. Contact him here.